Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.9
Journals
Sensors
Special Issues
Selected Papers from the 2022 IEEE International Workshop on Metrology...
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Selected Papers from the 2022 IEEE International Workshop on Metrology for Automotive

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 7413

Special Issue Editors

Dr. Stefano Cattini

E-Mail Website1 Website2
Guest Editor
Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, 41125 Modena, Italy
Interests: design and validation of measurement methods and measuring systems; measurements in automotive; biomedical measurements
Special Issues, Collections and Topics in MDPI journals
Dr. Federico Tramarin

E-Mail Website
Guest Editor
Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, 41125 Modena, Italy
Interests: real-time communications; industrial communication systems; industrial wireless communications; wireless sensors networks; performance measurements on networks; instrumentation and measurements; distributed measurement systems; time-sensitive networking; real-time embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2022 IEEE International Workshop on Metrology for Automotive (IEEE MetroAutomotive 2022) (https://www.metroautomotive.org/) will be held in Modena, Italy, on 4–6 July 2022.

The authors of papers presented at the workshop related to Sensors are invited to submit extended versions of their work to this Special Issue for publication.

MetroAutomotive 2022 aims to be a solid reference for the technical community to present and discuss the most recent results of scientific and technological research for the automotive industry, with particular emphasis on applications and new trends.

Topics:

  • Electronic instrumentation for automotive;
  • Automatic test equipment for automotive;
  • Sensors and sensor systems for automotive applications;
  • Wireless sensor networks in automotive;
  • Automotive instrumentation and telematics;
  • Diagnostics;
  • Standards for automotive instrumentation;
  • Legal and ethical implications of metrology in the future automotive field.

Dr. Stefano Cattini
Dr. Federico Tramarin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 5354 KiB  
Article
Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance
by Andrea Amidei, Susanna Spinsante, Grazia Iadarola, Simone Benatti, Federico Tramarin, Paolo Pavan and Luigi Rovati
Sensors 2023, 23(8), 4004; https://doi.org/10.3390/s23084004 - 15 Apr 2023
Cited by 5 | Viewed by 2854
Abstract
The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware [...] Read more.
The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness. Full article
(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Automotive)
►▼ Show Figures

Figure 1

19 pages, 7647 KiB  
Article
Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals
by Taraneh Aminosharieh Najafi, Antonio Affanni, Roberto Rinaldo and Pamela Zontone
Sensors 2023, 23(4), 2039; https://doi.org/10.3390/s23042039 - 11 Feb 2023
Cited by 8 | Viewed by 2641
Abstract
In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual [...] Read more.
In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects’ Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention. Full article
(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Automotive)
►▼ Show Figures

Figure 1

17 pages, 3421 KiB  
Article
HRTFs Measurement Based on Periodic Sequences Robust towards Nonlinearities in Automotive Audio
by Stefania Cecchi, Valeria Bruschi, Stefano Nobili, Alessandro Terenzi and Alberto Carini
Sensors 2023, 23(3), 1692; https://doi.org/10.3390/s23031692 - 3 Feb 2023
Cited by 1 | Viewed by 1322
Abstract
The head related transfer functions (HRTFs) represent the acoustic path transfer functions between sound sources in 3D space and the listener’s ear. They are used to create immersive audio scenarios or to subjectively evaluate sound systems according to a human-centric point of view. [...] Read more.
The head related transfer functions (HRTFs) represent the acoustic path transfer functions between sound sources in 3D space and the listener’s ear. They are used to create immersive audio scenarios or to subjectively evaluate sound systems according to a human-centric point of view. Cars are nowadays the most popular audio listening environment and the use of HRTFs in automotive audio has recently attracted the attention of researchers. In this context, the paper proposes a measurement method for HRTFs based on perfect or orthogonal periodic sequences. The proposed measurement method ensures robustness towards the nonlinearities that may affect the measurement system. The experimental results considering both an emulated scenario and real measurements in a controlled environment illustrate the effectiveness of the approach and compare the proposed method with other popular approaches. Full article
(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Automotive)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop